Tenforflow.js is an evolution ofdeeplearn.js, a Javascript library released by Google in August 2017. Deeplearn.js was born out of the success of the Tensorflow Playground, an interactive visualization of neural networks written in TypeScript.

Tensorflow.js has four layers: The WebGL API for GPU-supported
numerical operations, the web browser for user interactions, and two
APIs: Core and Layers.
The low-level Core API corresponds to the former deeplearn.js library.
It provides hardware-accelerated linear algebra operations and an eagerAPI
for automatic differentiation. The higher-level Layers API is used to
build machine-learning models on top of Core. The Layers API is modeled
after Keras and implements similar functionality. It also allows to import models previously trained in python with Keras or TensorFlow SavedModels and use it for inference or transfer learning in the browser.

With Tensorflow.js, machine-learning models can be utilized in the
browser in three ways: by importing already pre-trained models and using
them for inference only, by training models from scratch directly in
the browser, or by using transfer learning to first adapt imported models to the user's context and then use these improved models for inference.

As Nikhil Thorat and Daniel Smilkov, members of the Tensorflow team, point out in their announcement video, (see in the top of the post)
running Tensorflow in the browser has several advantages: the
infrastructure and set of requirements are simplified as the need for
background API requests is removed; the available data is richer in
nature thanks to newly accessible sensors, such as webcam and microphone
on computers or GPS and gyroscope on mobile devices; and the data also
remains on the client side which addresses privacy concerns. Read more...

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Hello, my name is Helge Scherlund and I am the Education Editor and Online Educator of this personal weblog and the founder of eLearning • Computer-Mediated Communication Center.
I have an education in the teaching adults and adult learning from Roskilde University, with Computer-Mediated Communication (CMC) and Human Resource Development (HRD) as specially studied subjects. I am the author of several articles and publications about the use of decision support tools, e-learning and computer-mediated communication. I am a member of The Danish Mathematical Society (DMF), The Danish Society for Theoretical Statistics (DSTS) and an individual member of the European Mathematical Society (EMS). Note: Comments published here are purely my own and do not reflect those of my current or future employers or other organizations.